
*This is the replication Stata do-file for the Supplementary Materials included in Berkay, Alica and Schakel, Arjan H. (2025) 'Does multilevel government increase legitimacy? Citizens' preferences for subnational authority and acceptance of governmental decisions,' Journal of Public Policy, forthcoming.

*Open dataset: Alica_Schakel_2025_JPP_replication_dataset.dta
{The following changes have been implemented to prepare the dataset produced by R for use in Stata.

encode(responseid), gen(id)

*tier is a recoded variable that traces the decision_maker: 1=N; 2=C; 3=M.
*treatment_nr is a recoded variable that traces the 'support treatment': 1=C; 2=C+N; 3=M; 4=M+C; 5=M+N; 6=N.
}


{APPENDIX B. Measuring preferences for subnational authority.

*IMPORTANT NOTE: Appendix B has its own replication dataset; OPEN Alica_Schakel_2025_JPP_replication_ICVS_dataset.dta

*The sources of the International Constitutional Value Survey (ICVS) are: 

*Brown, Alexander J., John Kincaid, Jacob Deem, and Richard Cole. 2016. Measuring citizen attachment to federal principles: Results from Australia, Canada, the United States, Germany and Great Britain. Paper presented at the 24th World Congress of Politics Science of the International Political Science Association, Poznan, Poland, July 2016. 

*Brown, Alexander J., Jacob Deem, and John Kincaid. 2018. Federal attachment and popular trust & confidence: Lessons from the International Constitutional Values Survey (Mark 2). Paper presented at the 25th World Congress of Politics Science of the International Political Science Association, Brisbane, Australia, July 2018.

*Brown, Alexander J., Jacob Deem and John Kincaid 2022. Federal constitutional values and citizens attitudes to government: Explaining federal system viability and reform preferences in eight countries. Publius: The Journal of Federalism 51(1): 1-25. 

**#Table B1. Descriptive statistics of survey items to tap preferences for subnational authority.

tabstat psa sf_intvl sh_intvl sf1 sf2 sf3 sh1 sh2 sh3 if tier==1, statistics( mean sd p25 median p75 min max ) columns(statistics)

**#Table B3a. Principal component analysis: Preference for subnational authority.

bysort country: factor sf1 sf2 sf3 sh1 sh2 sh3, pcf
bysort country: alpha sf1 sf2 sf3 sh1 sh2 sh3 

factor sf1 sf2 sf3 sh1 sh2 sh3, pcf
alpha sf1 sf2 sf3 sh1 sh2 sh3 

**#Table B3b. Principal component analysis: Preference for self-rule.
*The variable comparison is used to include only respondents who have answered all six survey items.

bysort country: factor sf1 sf2 sf3 if comparison==1, pcf
bysort country: alpha sf1 sf2 sf3  if comparison==1

factor sf1 sf2 sf3 if comparison==1, pcf
alpha sf1 sf2 sf3 if comparison==1

**#Table B3c. Principal component analysis: Preference for shared rule.
*The variable comparison is used to include only respondents who have answered all six survey items.

bysort country: factor sh1 sh2 sh3 if comparison==1, pcf
bysort country: alpha sh1 sh2 sh3  if comparison==1

factor sh1 sh2 sh3 if comparison==1, pcf
alpha sh1 sh2 sh3 if comparison==1

}

{APPENDIX C. Full model results.

*IMPORTANT NOTE: The point estimates are different from those produced by R and shown in the Supplementary Materials. This is because Stata and R take different (respondent) base categories. The differences between the point estimates are the same and lead to the same findings.

*The point estimates are statistically significantly different from each other at p<0.05 when their 84% CIs do not overlap and at p<0.01 when their 95% CIs do not overlap (Greenland et al. 2016; Julius 2004; Macgregor-Fors and Payton 2013). R and Stata produce slightly different CIs whereby estimates are different at the second (hundreths) or third (thousands) digit behind the decimal point. In some instances, this leads to some discrepancies between R and Stata in assigning statistical significance. In all cases of discrepancies, the R estimates are more conservative, i.e. R indicates non-significance and p<0.05 whereas Stata indicates respectively p<0.05 and p<0.01. The statistical significance levels shown in the Figures in the main text and in the Supplementary Materials are based on the more conservative R estimates. 

*Set dataset structure.

xtset id tier


**#Table C1. Model 1-Q1-Q3-Q5: Regression table.

xtreg first_answer ib(1).tier##c.psa, fe cluster(id)
xtreg first_answer ib(3).tier##c.psa, fe cluster(id)
xtreg first_answer ib(2).tier##c.psa, fe cluster(id)


**#Figure C1. Model 1-Q1-Q3-Q5: Marginal effects.

*IMPORTANT NOTE: Set the base categories for tier to get estimates for the government taking a decision.

*Figure C1A-C1B. Decision taken by the municipality.

xtreg first_answer ib(3).tier##c.psa, fe cluster(id)

margins, at( tier=( 1 2 3 ) psa=( 0 1 ) ) level(84) noestimcheck
margins, at( tier=( 1 2 3 ) psa=( 0 1 ) ) level(95) noestimcheck

margins, at( tier=( 1 2 3 ) psa=( 0 (0.1) 1 ) ) level(84) noestimcheck


*Figure C1C-C1D. Decision taken by the county.

xtreg first_answer ib(2).tier##c.psa, fe cluster(id)

margins, at( tier=( 1 2 3 ) psa=( 0 1 ) ) level(84) noestimcheck
margins, at( tier=( 1 2 3 ) psa=( 0 1 ) ) level(95) noestimcheck

margins, at( tier=( 1 2 3 ) psa=( 0 (0.1) 1 ) ) level(84) noestimcheck


*Figure C1E-C1F. Decision taken by the national government.

xtreg first_answer ib(1).tier##c.psa, fe cluster(id)

margins, at( tier=( 1 2 3 ) psa=( 0 1 ) ) level(84) noestimcheck
margins, at( tier=( 1 2 3 ) psa=( 0 1 ) ) level(95) noestimcheck

margins, at( tier=( 1 2 3 ) psa=( 0 (0.1) 1 ) ) level(84) noestimcheck


**#Table C2a. Model 2-Q2-Q4-Q6, base-category = municipality: Regression table.

xtreg second_answer ib(3).tier i.first_answer##ib(3).treatment_nr##c.psa, fe cluster(id)


**#Table C2b. Model 2-Q2-Q4-Q6, base-category = county: Regression table.

xtreg second_answer ib(2).tier i.first_answer##ib(1).treatment_nr##c.psa, fe cluster(id)


**#Table C2c. Model 2-Q2-Q4-Q6, base-category = national government: Regression table.

xtreg second_answer ib(1).tier i.first_answer##ib(6).treatment_nr##c.psa, fe cluster(id)
}

{APPENDIX D. Robustness test I: DV with five answer categories.
**IMPORTANT NOTE: The point estimates are different from those produced by R and shown in the Supplementary Materials. This is because Stata and R take different (respondent) base categories. The differences between the point estimates are the same and lead to the same findings.

*The point estimates are statistically significantly different from each other at p<0.05 when their 84% CIs do not overlap and at p<0.01 when their 95% CIs do not overlap (Greenland et al. 2016; Julius 2004; Macgregor-Fors and Payton 2013). R and Stata produce slightly different CIs whereby estimates are different at the second (hundreths) or third (thousands) digit behind the decimal point. In some instances, this leads to some discrepancies between R and Stata in assigning statistical significance. In all cases of discrepancies, the R estimates are more conservative, i.e. R indicates non-significance and p<0.05 whereas Stata indicates respectively p<0.05 and p<0.01. The statistical significance levels shown in the Figures in the main text and in the Supplementary Materials are based on the more conservative R estimates. 

***Set dataset structure.

xtset id tier

**#Table D1. Model 1-Q1-Q3-Q5: DV 5 answer categories.

xtreg first_answer_original ib(1).tier##c.psa, fe cluster(id)
xtreg first_answer_original ib(3).tier##c.psa, fe cluster(id)
xtreg first_answer_original ib(2).tier##c.psa, fe cluster(id)

**#Figure D1. Model 1-Q1-Q3-Q5: DV 5 answer categories.

*Figure D1A-D1B. Base category: municipality.

xtreg first_answer_original ib(3).tier##c.psa, fe cluster(id)

margins, at( tier=( 1 2 3 ) psa=( 0 1 ) ) level(84) noestimcheck
margins, at( tier=( 1 2 3 ) psa=( 0 1 ) ) level(95) noestimcheck

margins, at( tier=( 1 2 3 ) psa=( 0 (0.1) 1 ) ) level(84) noestimcheck

*Figure D1C-D1D. Base category: county.

xtreg first_answer_original ib(2).tier##c.psa, fe cluster(id)

margins, at( tier=( 1 2 3 ) psa=( 0 1 ) ) level(84) noestimcheck
margins, at( tier=( 1 2 3 ) psa=( 0 1 ) ) level(95) noestimcheck

margins, at( tier=( 1 2 3 ) psa=( 0 (0.1) 1 ) ) level(84) noestimcheck

*Figure D1E-D1F. Base category: national government.

xtreg first_answer_original ib(1).tier##c.psa, fe cluster(id)

margins, at( tier=( 1 2 3 ) psa=( 0 1 ) ) level(84) noestimcheck
margins, at( tier=( 1 2 3 ) psa=( 0 1 ) ) level(95) noestimcheck

margins, at( tier=( 1 2 3 ) psa=( 0 (0.1) 1 ) ) level(84) noestimcheck


**#Table D2a. Model 2-Q2-Q4-Q6-base-category = municipality: DV 5 answer categories.

xtreg second_answer_original ib(3).tier i.first_answer_original##ib(3).treatment_nr##c.psa, fe cluster(id)


**#Table D2b. Model 2-Q2-Q4-Q6-base-category = county: DV 5 answer categories.

xtreg second_answer_original ib(2).tier i.first_answer_original##ib(1).treatment_nr##c.psa, fe cluster(id)


**#Table D2c. Model 2-Q2-Q4-Q6-base-category = national government: DV 5 answer categories.

xtreg second_answer_original ib(1).tier i.first_answer_original##ib(6).treatment_nr##c.psa, fe cluster(id)


**#Figure D2. Model 2-Q2-Q4-Q6: DV 5 answer categories.
{
*IMPORTANT NOTE: The point estimates are different from those produced by R and shown in the Supplementary Materials. This is because Stata and R take different (respondent) base categories. The differences between the point estimates are the same and lead to the same findings.

*The point estimates are statistically significantly different from each other at p<0.05 when their 84% CIs do not overlap and at p<0.01 when their 95% CIs do not overlap (Greenland et al. 2016; Julius 2004; Macgregor-Fors and Payton 2013). R and Stata produce slightly different CIs whereby estimates are different at the second (hundreths) or third (thousands) digit behind the decimal point. In some instances, this leads to some discrepancies between R and Stata in assigning statistical significance. In all cases of discrepancies, the R estimates are more conservative, i.e. R indicates non-significance and p<0.05 whereas Stata indicates respectively p<0.05 and p<0.01. The statistical significance levels shown in the Figures in the main text and in the Supplementary Materials are based on the more conservative R estimates. 

**#Figure D2A-D2C. Municipality decides to close kindergarten.
{
*IMPORTANT NOTE: Set the base categories for tier and treatment_nr to get estimates for the municipality taking a decision.

xtreg second_answer_original ib(3).tier i.first_answer_original##ib(3).treatment_nr##c.psa, fe cluster(id)


*Figure D2A: Municipality decides to close kindergarten and county supports.

margins, at( tier=3 first_answer_original=1 treatment_nr==1 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer_original=2 treatment_nr==1 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer_original=3 treatment_nr==1 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer_original=4 treatment_nr==1 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer_original=5 treatment_nr==1 psa=( 0.44 0.88 ) ) level(84) noestimcheck

margins, at( tier=3 first_answer_original=1 treatment_nr==1 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer_original=2 treatment_nr==1 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer_original=3 treatment_nr==1 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer_original=4 treatment_nr==1 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer_original=5 treatment_nr==1 psa=( 0.44 0.88 ) ) level(95) noestimcheck


*Figure D2B: Municipality decides to close kindergarten and national government supports.

margins, at( tier=3 first_answer_original=1 treatment_nr==6 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer_original=2 treatment_nr==6 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer_original=3 treatment_nr==6 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer_original=4 treatment_nr==6 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer_original=5 treatment_nr==6 psa=( 0.44 0.88 ) ) level(84) noestimcheck

margins, at( tier=3 first_answer_original=1 treatment_nr==6 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer_original=2 treatment_nr==6 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer_original=3 treatment_nr==6 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer_original=4 treatment_nr==6 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer_original=5 treatment_nr==6 psa=( 0.44 0.88 ) ) level(95) noestimcheck


*Figure D2C: Municipality decides to close kindergarten and county and national government support.

margins, at( tier=3 first_answer_original=1 treatment_nr==2 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer_original=2 treatment_nr==2 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer_original=3 treatment_nr==2 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer_original=4 treatment_nr==2 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer_original=5 treatment_nr==2 psa=( 0.44 0.88 ) ) level(84) noestimcheck

margins, at( tier=3 first_answer_original=1 treatment_nr==2 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer_original=2 treatment_nr==2 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer_original=3 treatment_nr==2 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer_original=4 treatment_nr==2 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer_original=5 treatment_nr==2 psa=( 0.44 0.88 ) ) level(95) noestimcheck
}

**#Figure D2D-D2F. County decides to close upper secondary school.
{
*IMPORTANT NOTE: Set the base categories for tier and treatment_nr to get estimates for the county taking a decision.

xtreg second_answer_original ib(2).tier i.first_answer_original##ib(1).treatment_nr##c.psa, fe cluster(id)


*Figure D2D: County decides to close upper secondary school and municipality supports.

margins, at( tier=2 first_answer_original=1 treatment_nr==3 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer_original=2 treatment_nr==3 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer_original=3 treatment_nr==3 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer_original=4 treatment_nr==3 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer_original=5 treatment_nr==3 psa=( 0.44 0.88 ) ) level(84) noestimcheck

margins, at( tier=2 first_answer_original=1 treatment_nr==3 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer_original=2 treatment_nr==3 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer_original=3 treatment_nr==3 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer_original=4 treatment_nr==3 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer_original=5 treatment_nr==3 psa=( 0.44 0.88 ) ) level(95) noestimcheck

*Figure D2E: County decides to close upper secondary school and national government supports.

margins, at( tier=2 first_answer_original=1 treatment_nr==6 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer_original=2 treatment_nr==6 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer_original=3 treatment_nr==6 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer_original=4 treatment_nr==6 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer_original=5 treatment_nr==6 psa=( 0.44 0.88 ) ) level(84) noestimcheck

margins, at( tier=2 first_answer_original=1 treatment_nr==6 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer_original=2 treatment_nr==6 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer_original=3 treatment_nr==6 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer_original=4 treatment_nr==6 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer_original=5 treatment_nr==6 psa=( 0.44 0.88 ) ) level(95) noestimcheck


*Figure D2F: County decides to close upper secondary school and municipality and national government support.

margins, at( tier=2 first_answer_original=1 treatment_nr==5 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer_original=2 treatment_nr==5 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer_original=3 treatment_nr==5 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer_original=4 treatment_nr==5 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer_original=5 treatment_nr==5 psa=( 0.44 0.88 ) ) level(84) noestimcheck

margins, at( tier=2 first_answer_original=1 treatment_nr==5 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer_original=2 treatment_nr==5 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer_original=3 treatment_nr==5 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer_original=4 treatment_nr==5 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer_original=5 treatment_nr==5 psa=( 0.44 0.88 ) ) level(95) noestimcheck
}

**#Figure D2G-D2I. National government decides to close a department of a university (college).
{
*IMPORTANT NOTE: Set the base categories for tier and treatment_nr to get estimates for the national government taking a decision.

xtreg second_answer_original ib(1).tier i.first_answer_original##ib(6).treatment_nr##c.psa, fe cluster(id)


*Figure D2G: National government decides to close a department of a university (college) and municipality supports.

margins, at( tier=1 first_answer_original=1 treatment_nr==3 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer_original=2 treatment_nr==3 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer_original=3 treatment_nr==3 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer_original=4 treatment_nr==3 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer_original=5 treatment_nr==3 psa=( 0.44 0.88 ) ) level(84) noestimcheck

margins, at( tier=1 first_answer_original=1 treatment_nr==3 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer_original=2 treatment_nr==3 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer_original=3 treatment_nr==3 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer_original=4 treatment_nr==3 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer_original=5 treatment_nr==3 psa=( 0.44 0.88 ) ) level(95) noestimcheck


*Figure D2H: National government decides to close a department of a university (college) and county supports.

margins, at( tier=1 first_answer_original=1 treatment_nr==1 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer_original=2 treatment_nr==1 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer_original=3 treatment_nr==1 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer_original=4 treatment_nr==1 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer_original=5 treatment_nr==1 psa=( 0.44 0.88 ) ) level(84) noestimcheck

margins, at( tier=1 first_answer_original=1 treatment_nr==1 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer_original=2 treatment_nr==1 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer_original=3 treatment_nr==1 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer_original=4 treatment_nr==1 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer_original=5 treatment_nr==1 psa=( 0.44 0.88 ) ) level(95) noestimcheck


*Figure D2I: National government decides to close a department of a university (college) and municipality and county support.

margins, at( tier=1 first_answer_original=1 treatment_nr==4 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer_original=2 treatment_nr==4 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer_original=3 treatment_nr==4 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer_original=4 treatment_nr==4 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer_original=5 treatment_nr==4 psa=( 0.44 0.88 ) ) level(84) noestimcheck

margins, at( tier=1 first_answer_original=1 treatment_nr==4 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer_original=2 treatment_nr==4 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer_original=3 treatment_nr==4 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer_original=4 treatment_nr==4 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer_original=5 treatment_nr==4 psa=( 0.44 0.88 ) ) level(95) noestimcheck

}
}
}

{APPENDIX E. Robustness test II: Preferences for self-rule and preferences for shared rule.

*IMPORTANT NOTE
{
*IMPORTANT NOTE: The point estimates are different from those produced by R and shown in the Supplementary Materials. This is because Stata and R take different (respondent) base categories. The differences between the point estimates are the same and lead to the same findings.

*The point estimates are statistically significantly different from each other at p<0.05 when their 84% CIs do not overlap and at p<0.01 when their 95% CIs do not overlap (Greenland et al. 2016; Julius 2004; Macgregor-Fors and Payton 2013). R and Stata produce slightly different CIs whereby estimates are different at the second (hundreths) or third (thousands) digit behind the decimal point. In some instances, this leads to some discrepancies between R and Stata in assigning statistical significance. In all cases of discrepancies, the R estimates are more conservative, i.e. R indicates non-significance and p<0.05 whereas Stata indicates respectively p<0.05 and p<0.01. The statistical significance levels shown in the Figures in the main text and in the Supplementary Materials are based on the more conservative R estimates. 

*Set dataset structure.

xtset id tier
}

**#Table E1A. Model 1-Q1-Q3-Q5: Preference for self-rule.
{
*IMPORTANT NOTE: Set the base categories for tier to get estimates for the government taking a decision.

xtreg first_answer ib(3).tier##c.sf_intvl, fe cluster(id)
xtreg first_answer ib(2).tier##c.sf_intvl, fe cluster(id)
xtreg first_answer ib(1).tier##c.sf_intvl, fe cluster(id)
}
**#Figure E1A. Model 1-Q1-Q3-Q5: Preference for self-rule.
{
*IMPORTANT NOTE: The point estimates are different from those produced by R and shown in the Supplementary Materials. This is because Stata and R take different (respondent) base categories. The differences between the point estimates are the same and lead to the same findings.

*The point estimates are statistically significantly different from each other at p<0.05 when their 84% CIs do not overlap and at p<0.01 when their 95% CIs do not overlap (Greenland et al. 2016; Julius 2004; Macgregor-Fors and Payton 2013). R and Stata produce slightly different CIs whereby estimates are different at the second (hundreths) or third (thousands) digit behind the decimal point. In some instances, this leads to some discrepancies between R and Stata in assigning statistical significance. In all cases of discrepancies, the R estimates are more conservative, i.e. R indicates non-significance and p<0.05 whereas Stata indicates respectively p<0.05 and p<0.01. The statistical significance levels shown in the Figures in the main text and in the Supplementary Materials are based on the more conservative R estimates. 

*Set the base categories for tier to get estimates for the government taking a decision.

*Figure E1Aa-E1Ab. Decision taken by the municipality to close a kindergarten.
{
xtreg first_answer ib(3).tier##c.sf_intvl, fe cluster(id)

margins, at( tier=( 1 2 3 ) sf_intvl=( 0 1 ) ) level(84) noestimcheck
margins, at( tier=( 1 2 3 ) sf_intvl=( 0 1 ) ) level(95) noestimcheck

margins, at( tier=( 1 2 3 ) sf_intvl=( 0 (0.1) 1 ) ) level(84) noestimcheck
}
*Figure E1Ac-E1Ad. Decision taken by the county to close an upper secondary school.
{
xtreg first_answer ib(2).tier##c.sf_intvl, fe cluster(id)

margins, at( tier=( 1 2 3 ) sf_intvl=( 0 1 ) ) level(84) noestimcheck
margins, at( tier=( 1 2 3 ) sf_intvl=( 0 1 ) ) level(95) noestimcheck

margins, at( tier=( 1 2 3 ) sf_intvl=( 0 (0.1) 1 ) ) level(84) noestimcheck
}
*Figure E1Ae-E1Af. Decision taken by the national government to close a department of a university (college).
{
xtreg first_answer ib(1).tier##c.sf_intvl, fe cluster(id)

margins, at( tier=( 1 2 3 ) sf_intvl=( 0 1 ) ) level(84) noestimcheck
margins, at( tier=( 1 2 3 ) sf_intvl=( 0 1 ) ) level(95) noestimcheck

margins, at( tier=( 1 2 3 ) sf_intvl=( 0 (0.1) 1 ) ) level(84) noestimcheck
}
}

**#Table E1B. Model 1-Q1-Q3-Q5: Preference for shared rule.
{
*IMPORTANT NOTE: Set the base categories for tier to get estimates for the government taking a decision.

xtreg first_answer ib(3).tier##c.sh_intvl, fe cluster(id)
xtreg first_answer ib(2).tier##c.sh_intvl, fe cluster(id)
xtreg first_answer ib(1).tier##c.sh_intvl, fe cluster(id)
}
**#Figure E1B. Model 1-Q1-Q3-Q5: Preference for shared rule.
{
*IMPORTANT NOTE: The point estimates are different from those produced by R and shown in the Supplementary Materials. This is because Stata and R take different (respondent) base categories. The differences between the point estimates are the same and lead to the same findings.

*The point estimates are statistically significantly different from each other at p<0.05 when their 84% CIs do not overlap and at p<0.01 when their 95% CIs do not overlap (Greenland et al. 2016; Julius 2004; Macgregor-Fors and Payton 2013). R and Stata produce slightly different CIs whereby estimates are different at the second (hundreths) or third (thousands) digit behind the decimal point. In some instances, this leads to some discrepancies between R and Stata in assigning statistical significance. In all cases of discrepancies, the R estimates are more conservative, i.e. R indicates non-significance and p<0.05 whereas Stata indicates respectively p<0.05 and p<0.01. The statistical significance levels shown in the Figures in the main text and in the Supplementary Materials are based on the more conservative R estimates. 

*Set the base categories for tier to get estimates for the government taking a decision.

*Figure E1Ba-E1Bb. Decision taken by the municipality.*
{
xtreg first_answer ib(3).tier##c.sh_intvl, fe cluster(id)

margins, at( tier=( 1 2 3 ) sh_intvl=( 0 1 ) ) level(84) noestimcheck
margins, at( tier=( 1 2 3 ) sh_intvl=( 0 1 ) ) level(95) noestimcheck

margins, at( tier=( 1 2 3 ) sh_intvl=( 0 (0.1) 1 ) ) level(84) noestimcheck
}
*Figure E1Bc-E1Bd. Decision taken by the county.
{
xtreg first_answer ib(2).tier##c.sh_intvl, fe cluster(id)

margins, at( tier=( 1 2 3 ) sh_intvl=( 0 1 ) ) level(84) noestimcheck
margins, at( tier=( 1 2 3 ) sh_intvl=( 0 1 ) ) level(95) noestimcheck

margins, at( tier=( 1 2 3 ) sh_intvl=( 0 (0.1) 1 ) ) level(84) noestimcheck
}
*Figure E1Be-E1Bf. Decision taken by the national government.
{
xtreg first_answer ib(1).tier##c.sh_intvl, fe cluster(id)

margins, at( tier=( 1 2 3 ) sh_intvl=( 0 1 ) ) level(84) noestimcheck
margins, at( tier=( 1 2 3 ) sh_intvl=( 0 1 ) ) level(95) noestimcheck

margins, at( tier=( 1 2 3 ) sh_intvl=( 0 (0.1) 1 ) ) level(84) noestimcheck
}
}

**#Table E1C. Model 1-Q1-Q3-Q5: Preference for each item.
{
*IMPORTANT NOTE: Set the base categories for tier to get estimates for the government taking a decision.

xtreg first_answer ib(3).tier##c.sf1, fe cluster(id)
xtreg first_answer ib(3).tier##c.sf2, fe cluster(id)
xtreg first_answer ib(3).tier##c.sf3, fe cluster(id)
xtreg first_answer ib(3).tier##c.sh1, fe cluster(id)
xtreg first_answer ib(3).tier##c.sh2, fe cluster(id)
xtreg first_answer ib(3).tier##c.sh3, fe cluster(id)

xtreg first_answer ib(2).tier##c.sf1, fe cluster(id)
xtreg first_answer ib(2).tier##c.sf2, fe cluster(id)
xtreg first_answer ib(2).tier##c.sf3, fe cluster(id)
xtreg first_answer ib(2).tier##c.sh1, fe cluster(id)
xtreg first_answer ib(2).tier##c.sh2, fe cluster(id)
xtreg first_answer ib(2).tier##c.sh3, fe cluster(id)

xtreg first_answer ib(1).tier##c.sf1, fe cluster(id)
xtreg first_answer ib(1).tier##c.sf2, fe cluster(id)
xtreg first_answer ib(1).tier##c.sf3, fe cluster(id)
xtreg first_answer ib(1).tier##c.sh1, fe cluster(id)
xtreg first_answer ib(1).tier##c.sh2, fe cluster(id)
xtreg first_answer ib(1).tier##c.sh3, fe cluster(id)
}
**#Figure E1CA. Model 1-Q1-Q3-Q5-base-cat. = M: Preference for self-rule items 1-3.
{
*IMPORTANT NOTE: The point estimates are different from those produced by R and shown in the Supplementary Materials. This is because Stata and R take different (respondent) base categories. The differences between the point estimates are the same and lead to the same findings.

*The point estimates are statistically significantly different from each other at p<0.05 when their 84% CIs do not overlap and at p<0.01 when their 95% CIs do not overlap (Greenland et al. 2016; Julius 2004; Macgregor-Fors and Payton 2013). R and Stata produce slightly different CIs whereby estimates are different at the second (hundreths) or third (thousands) digit behind the decimal point. In some instances, this leads to some discrepancies between R and Stata in assigning statistical significance. In all cases of discrepancies, the R estimates are more conservative, i.e. R indicates non-significance and p<0.05 whereas Stata indicates respectively p<0.05 and p<0.01. The statistical significance levels shown in the Figures in the main text and in the Supplementary Materials are based on the more conservative R estimates. 

*Set the base categories for tier to get estimates for the government taking a decision.

*Figure E1CAa-E1CAb. Decision by the municipality to close down kindergarten: self-rule item 1.
{
xtreg first_answer ib(3).tier##c.sf1, fe cluster(id)

margins, at( tier=( 1 2 3 ) sf1=( 1 4 ) ) level(84) noestimcheck
margins, at( tier=( 1 2 3 ) sf1=( 1 4 ) ) level(95) noestimcheck

margins, at( tier=( 1 2 3 ) sf1=( 1 (0.2) 4 ) ) level(84) noestimcheck
}
*Figure E1CAc-E1CAd. Decision by the municipality to close down kindergarten: self-rule item 2.
{
xtreg first_answer ib(3).tier##c.sf2, fe cluster(id)

margins, at( tier=( 1 2 3 ) sf2=( 1 4 ) ) level(84) noestimcheck
margins, at( tier=( 1 2 3 ) sf2=( 1 4 ) ) level(95) noestimcheck

margins, at( tier=( 1 2 3 ) sf2=( 1 (0.2) 4 ) ) level(84) noestimcheck
}
*Figure E1CAe-E1CAf. Decision by the municipality to close down kindergarten: self-rule item 3.
{
xtreg first_answer ib(3).tier##c.sf3, fe cluster(id)

margins, at( tier=( 1 2 3 ) sf3=( 1 4 ) ) level(84) noestimcheck
margins, at( tier=( 1 2 3 ) sf3=( 1 4 ) ) level(95) noestimcheck

margins, at( tier=( 1 2 3 ) sf3=( 1 (0.2) 4 ) ) level(84) noestimcheck
}
}
**#Figure E1CB. Model 1-Q1-Q3-Q5-base-cat. = C: Preference for self-rule items 1-3.
{
*IMPORTANT NOTE: The point estimates are different from those produced by R and shown in the Supplementary Materials. This is because Stata and R take different (respondent) base categories. The differences between the point estimates are the same and lead to the same findings.

*The point estimates are statistically significantly different from each other at p<0.05 when their 84% CIs do not overlap and at p<0.01 when their 95% CIs do not overlap (Greenland et al. 2016; Julius 2004; Macgregor-Fors and Payton 2013). R and Stata produce slightly different CIs whereby estimates are different at the second (hundreths) or third (thousands) digit behind the decimal point. In some instances, this leads to some discrepancies between R and Stata in assigning statistical significance. In all cases of discrepancies, the R estimates are more conservative, i.e. R indicates non-significance and p<0.05 whereas Stata indicates respectively p<0.05 and p<0.01. The statistical significance levels shown in the Figures in the main text and in the Supplementary Materials are based on the more conservative R estimates. 

*Set the base categories for tier to get estimates for the government taking a decision.

*Figure E1CBa-E1CBb. Decision taken by the county to close upper secondary school: self-rule item 1.
{
xtreg first_answer ib(2).tier##c.sf1, fe cluster(id)

margins, at( tier=( 1 2 3 ) sf1=( 1 4 ) ) level(84) noestimcheck
margins, at( tier=( 1 2 3 ) sf1=( 1 4 ) ) level(95) noestimcheck

margins, at( tier=( 1 2 3 ) sf1=( 1 (0.2) 4 ) ) level(84) noestimcheck
}
*Figure E1CBc-E1CBd. Decision taken by the county to close upper secondary school: self-rule item 2.
{
xtreg first_answer ib(2).tier##c.sf2, fe cluster(id)

margins, at( tier=( 1 2 3 ) sf2=( 1 4 ) ) level(84) noestimcheck
margins, at( tier=( 1 2 3 ) sf2=( 1 4 ) ) level(95) noestimcheck

margins, at( tier=( 1 2 3 ) sf2=( 1 (0.2) 4 ) ) level(84) noestimcheck
}
*Figure E1CBe-E1CBf. Decision taken by the county to close upper secondary school: self-rule item 3.
{
xtreg first_answer ib(2).tier##c.sf3, fe cluster(id)

margins, at( tier=( 1 2 3 ) sf3=( 1 4 ) ) level(84) noestimcheck
margins, at( tier=( 1 2 3 ) sf3=( 1 4 ) ) level(95) noestimcheck

margins, at( tier=( 1 2 3 ) sf3=( 1 (0.2) 4 ) ) level(84) noestimcheck
}
}
**#Figure E1CC. Model 1-Q1-Q3-Q5-base-cat. = N: Preference for self-rule items 1-3.
{
*IMPORTANT NOTE: The point estimates are different from those produced by R and shown in the Supplementary Materials. This is because Stata and R take different (respondent) base categories. The differences between the point estimates are the same and lead to the same findings.

*The point estimates are statistically significantly different from each other at p<0.05 when their 84% CIs do not overlap and at p<0.01 when their 95% CIs do not overlap (Greenland et al. 2016; Julius 2004; Macgregor-Fors and Payton 2013). R and Stata produce slightly different CIs whereby estimates are different at the second (hundreths) or third (thousands) digit behind the decimal point. In some instances, this leads to some discrepancies between R and Stata in assigning statistical significance. In all cases of discrepancies, the R estimates are more conservative, i.e. R indicates non-significance and p<0.05 whereas Stata indicates respectively p<0.05 and p<0.01. The statistical significance levels shown in the Figures in the main text and in the Supplementary Materials are based on the more conservative R estimates. 

*Set the base categories for tier to get estimates for the government taking a decision.

*Figure E1CCa-E1CCb. Decision by the national government to close a department of an university (college): self-rule item 1.
{
xtreg first_answer ib(1).tier##c.sf1, fe cluster(id)

margins, at( tier=( 1 2 3 ) sf1=( 1 4 ) ) level(84) noestimcheck
margins, at( tier=( 1 2 3 ) sf1=( 1 4 ) ) level(95) noestimcheck

margins, at( tier=( 1 2 3 ) sf1=( 1 (0.2) 4 ) ) level(84) noestimcheck
}
*Figure E1CCc-E1CCd. Decision by the national government to close a department of an university (college): self-rule item 2.
{
xtreg first_answer ib(1).tier##c.sf2, fe cluster(id)

margins, at( tier=( 1 2 3 ) sf2=( 1 4 ) ) level(84) noestimcheck
margins, at( tier=( 1 2 3 ) sf2=( 1 4 ) ) level(95) noestimcheck

margins, at( tier=( 1 2 3 ) sf2=( 1 (0.2) 4 ) ) level(84) noestimcheck
}
*Figure E1CCe-E1CCf. Decision by the national government to close a department of an university (college): self-rule item 3.
{
xtreg first_answer ib(1).tier##c.sf3, fe cluster(id)

margins, at( tier=( 1 2 3 ) sf3=( 1 4 ) ) level(84) noestimcheck
margins, at( tier=( 1 2 3 ) sf3=( 1 4 ) ) level(95) noestimcheck

margins, at( tier=( 1 2 3 ) sf3=( 1 (0.2) 4 ) ) level(84) noestimcheck
}
}
**#Figure E1CD. Model 1-Q1-Q3-Q5-base-cat. = M: Preference for shared rule items 4-6.
{
*IMPORTANT NOTE: The point estimates are different from those produced by R and shown in the Supplementary Materials. This is because Stata and R take different (respondent) base categories. The differences between the point estimates are the same and lead to the same findings.

*The point estimates are statistically significantly different from each other at p<0.05 when their 84% CIs do not overlap and at p<0.01 when their 95% CIs do not overlap (Greenland et al. 2016; Julius 2004; Macgregor-Fors and Payton 2013). R and Stata produce slightly different CIs whereby estimates are different at the second (hundreths) or third (thousands) digit behind the decimal point. In some instances, this leads to some discrepancies between R and Stata in assigning statistical significance. In all cases of discrepancies, the R estimates are more conservative, i.e. R indicates non-significance and p<0.05 whereas Stata indicates respectively p<0.05 and p<0.01. The statistical significance levels shown in the Figures in the main text and in the Supplementary Materials are based on the more conservative R estimates. 

*Set the base categories for tier to get estimates for the government taking a decision.

*Figure E1CDa-E1CDb. Decision by the municipality to close down kindergarten: shared rule item 1.
{
xtreg first_answer ib(3).tier##c.sh1, fe cluster(id)

margins, at( tier=( 1 2 3 ) sh1=( 1 4 ) ) level(84) noestimcheck
margins, at( tier=( 1 2 3 ) sh1=( 1 4 ) ) level(95) noestimcheck

margins, at( tier=( 1 2 3 ) sh1=( 1 (0.2) 4 ) ) level(84) noestimcheck
}
*Figure E1CDc-E1CDd. Decision by the municipality to close down kindergarten: shared rule item 2.
{
xtreg first_answer ib(3).tier##c.sh2, fe cluster(id)

margins, at( tier=( 1 2 3 ) sh2=( 1 4 ) ) level(84) noestimcheck
margins, at( tier=( 1 2 3 ) sh2=( 1 4 ) ) level(95) noestimcheck

margins, at( tier=( 1 2 3 ) sh2=( 1 (0.2) 4 ) ) level(84) noestimcheck
}
*Figure E1CDe-E1CDf. Decision by the municipality to close down kindergarten: shared rule item 3.
{
xtreg first_answer ib(3).tier##c.sh3, fe cluster(id)

margins, at( tier=( 1 2 3 ) sh3=( 1 4 ) ) level(84) noestimcheck
margins, at( tier=( 1 2 3 ) sh3=( 1 4 ) ) level(95) noestimcheck

margins, at( tier=( 1 2 3 ) sh3=( 1 (0.2) 4 ) ) level(84) noestimcheck
}
}
**#Figure E1CE. Model 1-Q1-Q3-Q5-base-cat. = C: Preference for shared rule items 4-6.
{
*IMPORTANT NOTE: The point estimates are different from those produced by R and shown in the Supplementary Materials. This is because Stata and R take different (respondent) base categories. The differences between the point estimates are the same and lead to the same findings.

*The point estimates are statistically significantly different from each other at p<0.05 when their 84% CIs do not overlap and at p<0.01 when their 95% CIs do not overlap (Greenland et al. 2016; Julius 2004; Macgregor-Fors and Payton 2013). R and Stata produce slightly different CIs whereby estimates are different at the second (hundreths) or third (thousands) digit behind the decimal point. In some instances, this leads to some discrepancies between R and Stata in assigning statistical significance. In all cases of discrepancies, the R estimates are more conservative, i.e. R indicates non-significance and p<0.05 whereas Stata indicates respectively p<0.05 and p<0.01. The statistical significance levels shown in the Figures in the main text and in the Supplementary Materials are based on the more conservative R estimates. 

*Set the base categories for tier to get estimates for the government taking a decision.

*Figure E1CEa-E1CEb. Decision taken by the county to close upper secondary school: shared rule item 1.
{
xtreg first_answer ib(2).tier##c.sh1, fe cluster(id)

margins, at( tier=( 1 2 3 ) sh1=( 1 4 ) ) level(84) noestimcheck
margins, at( tier=( 1 2 3 ) sh1=( 1 4 ) ) level(95) noestimcheck

margins, at( tier=( 1 2 3 ) sh1=( 1 (0.2) 4 ) ) level(84) noestimcheck
}
*Figure E1CEc-E1CEd. Decision taken by the county to close upper secondary school: shared rule item 2.
{
xtreg first_answer ib(2).tier##c.sh2, fe cluster(id)

margins, at( tier=( 1 2 3 ) sh2=( 1 4 ) ) level(84) noestimcheck
margins, at( tier=( 1 2 3 ) sh2=( 1 4 ) ) level(95) noestimcheck

margins, at( tier=( 1 2 3 ) sh2=( 1 (0.2) 4 ) ) level(84) noestimcheck
}
*Figure E1CEe-E1CEf. Decision taken by the county to close upper secondary school: shared rule item 3.
{
xtreg first_answer ib(2).tier##c.sh3, fe cluster(id)

margins, at( tier=( 1 2 3 ) sh3=( 1 4 ) ) level(84) noestimcheck
margins, at( tier=( 1 2 3 ) sh3=( 1 4 ) ) level(95) noestimcheck

margins, at( tier=( 1 2 3 ) sh3=( 1 (0.2) 4 ) ) level(84) noestimcheck
}
}
**#Figure E1CF. Model 1-Q1-Q3-Q5-base-cat. = N: Preference for shared rule items 4-6.
{
*IMPORTANT NOTE: The point estimates are different from those produced by R and shown in the Supplementary Materials. This is because Stata and R take different (respondent) base categories. The differences between the point estimates are the same and lead to the same findings.

*The point estimates are statistically significantly different from each other at p<0.05 when their 84% CIs do not overlap and at p<0.01 when their 95% CIs do not overlap (Greenland et al. 2016; Julius 2004; Macgregor-Fors and Payton 2013). R and Stata produce slightly different CIs whereby estimates are different at the second (hundreths) or third (thousands) digit behind the decimal point. In some instances, this leads to some discrepancies between R and Stata in assigning statistical significance. In all cases of discrepancies, the R estimates are more conservative, i.e. R indicates non-significance and p<0.05 whereas Stata indicates respectively p<0.05 and p<0.01. The statistical significance levels shown in the Figures in the main text and in the Supplementary Materials are based on the more conservative R estimates. 

*Set the base categories for tier to get estimates for the government taking a decision.

*Figure E1CFa-E1CFb. Decision by the national government to close a department of an university (college): shared rule item 1.
{
xtreg first_answer ib(1).tier##c.sh1, fe cluster(id)

margins, at( tier=( 1 2 3 ) sh1=( 1 4 ) ) level(84) noestimcheck
margins, at( tier=( 1 2 3 ) sh1=( 1 4 ) ) level(95) noestimcheck

margins, at( tier=( 1 2 3 ) sh1=( 1 (0.2) 4 ) ) level(84) noestimcheck
}
*Figure E1CFc-E1CFd. Decision by the national government to close a department of an university (college): shared rule item 2.
{
xtreg first_answer ib(1).tier##c.sh2, fe cluster(id)

margins, at( tier=( 1 2 3 ) sh2=( 1 4 ) ) level(84) noestimcheck
margins, at( tier=( 1 2 3 ) sh2=( 1 4 ) ) level(95) noestimcheck

margins, at( tier=( 1 2 3 ) sh2=( 1 (0.2) 4 ) ) level(84) noestimcheck
}
*Figure E1CFe-E1CFf. Decision by the national government to close a department of an university (college): shared rule item 3.
{
xtreg first_answer ib(1).tier##c.sh3, fe cluster(id)

margins, at( tier=( 1 2 3 ) sh3=( 1 4 ) ) level(84) noestimcheck
margins, at( tier=( 1 2 3 ) sh3=( 1 4 ) ) level(95) noestimcheck

margins, at( tier=( 1 2 3 ) sh3=( 1 (0.2) 4 ) ) level(84) noestimcheck
}
}

**#Tables E2a-E2c
{
**#Table E2a. Model 2-Q2-Q4-Q6-base category = municipal government: Preference for each item.

xtreg second_answer ib(3).tier i.first_answer##ib(3).treatment_nr##c.sf1, fe cluster(id)
xtreg second_answer ib(3).tier i.first_answer##ib(3).treatment_nr##c.sf2, fe cluster(id)
xtreg second_answer ib(3).tier i.first_answer##ib(3).treatment_nr##c.sf3, fe cluster(id)
xtreg second_answer ib(3).tier i.first_answer##ib(3).treatment_nr##c.sh1, fe cluster(id)
xtreg second_answer ib(3).tier i.first_answer##ib(3).treatment_nr##c.sh2, fe cluster(id)
xtreg second_answer ib(3).tier i.first_answer##ib(3).treatment_nr##c.sh3, fe cluster(id)

**#Table E2b. Model 2-Q2-Q4-Q6-base category = county government: Preference for each item.

xtreg second_answer ib(2).tier i.first_answer##ib(1).treatment_nr##c.sf1, fe cluster(id)
xtreg second_answer ib(2).tier i.first_answer##ib(1).treatment_nr##c.sf2, fe cluster(id)
xtreg second_answer ib(2).tier i.first_answer##ib(1).treatment_nr##c.sf3, fe cluster(id)
xtreg second_answer ib(2).tier i.first_answer##ib(1).treatment_nr##c.sh1, fe cluster(id)
xtreg second_answer ib(2).tier i.first_answer##ib(1).treatment_nr##c.sh2, fe cluster(id)
xtreg second_answer ib(2).tier i.first_answer##ib(1).treatment_nr##c.sh3, fe cluster(id)

**#Table E2c. Model 2-Q2-Q4-Q6-base category = national government: Preference for each item.

xtreg second_answer ib(1).tier i.first_answer##ib(6).treatment_nr##c.sf1, fe cluster(id)
xtreg second_answer ib(1).tier i.first_answer##ib(6).treatment_nr##c.sf2, fe cluster(id)
xtreg second_answer ib(1).tier i.first_answer##ib(6).treatment_nr##c.sf3, fe cluster(id)
xtreg second_answer ib(1).tier i.first_answer##ib(6).treatment_nr##c.sh1, fe cluster(id)
xtreg second_answer ib(1).tier i.first_answer##ib(6).treatment_nr##c.sh2, fe cluster(id)
xtreg second_answer ib(1).tier i.first_answer##ib(6).treatment_nr##c.sh3, fe cluster(id)
}
**#Figure E2A. Model 2-Q2-Q4-Q6: Preference for self-rule item 1.
{

xtset id tier

*IMPORTANT NOTE: The point estimates are different from those produced by R and shown in the Supplementary Materials. This is because Stata and R take different (respondent) base categories. The differences between the point estimates are the same and lead to the same findings.

*The point estimates are statistically significantly different from each other at p<0.05 when their 84% CIs do not overlap and at p<0.01 when their 95% CIs do not overlap (Greenland et al. 2016; Julius 2004; Macgregor-Fors and Payton 2013). R and Stata produce slightly different CIs whereby estimates are different at the second (hundreths) or third (thousands) digit behind the decimal point. In some instances, this leads to some discrepancies between R and Stata in assigning statistical significance. In all cases of discrepancies, the R estimates are more conservative, i.e. R indicates non-significance and p<0.05 whereas Stata indicates respectively p<0.05 and p<0.01. The statistical significance levels shown in the Figures in the main text and in the Supplementary Materials are based on the more conservative R estimates. 

**#Figures E2Aa-E2Ac: Municipality decides to close kindergarten.
{
*IMPORTANT NOTE: Set the base categories for tier and treatment_nr to get estimates for the municipality taking a decision.

xtreg second_answer ib(3).tier i.first_answer##ib(3).treatment_nr##c.sf1, fe cluster(id)

*Figure E2Aa: Municipality decides to close kindergarten and county supports.
{
margins, at( tier=3 first_answer=1 treatment_nr==1 sf1=( 2.5 3.93 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==1 sf1=( 2.5 3.93 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==1 sf1=( 2.5 3.93 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==1 sf1=( 2.5 3.93 ) ) level(84) noestimcheck

margins, at( tier=3 first_answer=1 treatment_nr==1 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==1 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==1 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==1 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
}
*Figure E2Ab: Municipality decides to close kindergarten and national government supports.
{
margins, at( tier=3 first_answer=1 treatment_nr==6 sf1=( 2.5 3.93 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==6 sf1=( 2.5 3.93 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==6 sf1=( 2.5 3.93 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==6 sf1=( 2.5 3.93 ) ) level(84) noestimcheck

margins, at( tier=3 first_answer=1 treatment_nr==6 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==6 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==6 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==6 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
}
*Figure E2Ac: Municipality decides to close kindergarten and county and national government support.
{
margins, at( tier=3 first_answer=1 treatment_nr==2 sf1=( 2.5 3.93 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==2 sf1=( 2.5 3.93 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==2 sf1=( 2.5 3.93 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==2 sf1=( 2.5 3.93 ) ) level(84) noestimcheck

margins, at( tier=3 first_answer=1 treatment_nr==2 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==2 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==2 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==2 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
}
}
**#Figures E2Ad-E2Af: County decides to close upper secondary school.
{
*IMPORTANT NOTE: Set the base categories for tier and treatment_nr to get estimates for the county taking a decision.

xtreg second_answer ib(2).tier i.first_answer##ib(1).treatment_nr##c.sf1, fe cluster(id)

*Figure E2Ad: County decides to close upper secondary school and municipality supports.
{
margins, at( tier=2 first_answer=1 treatment_nr==3 sf1=( 2.5 3.93 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==3 sf1=( 2.5 3.93 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==3 sf1=( 2.5 3.93 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==3 sf1=( 2.5 3.93 ) ) level(84) noestimcheck

margins, at( tier=2 first_answer=1 treatment_nr==3 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==3 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==3 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==3 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
}
*Figure E2Ae: County decides to close upper secondary school and national government supports.
{
margins, at( tier=2 first_answer=1 treatment_nr==6 sf1=( 2.5 3.93 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==6 sf1=( 2.5 3.93 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==6 sf1=( 2.5 3.93 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==6 sf1=( 2.5 3.93 ) ) level(84) noestimcheck

margins, at( tier=2 first_answer=1 treatment_nr==6 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==6 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==6 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==6 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
}
*Figure E2Af: County decides to close upper secondary school and municipality and national government support.
{
margins, at( tier=2 first_answer=1 treatment_nr==5 sf1=( 2.5 3.93 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==5 sf1=( 2.5 3.93 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==5 sf1=( 2.5 3.93 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==5 sf1=( 2.5 3.93 ) ) level(84) noestimcheck

margins, at( tier=2 first_answer=1 treatment_nr==5 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==5 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==5 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==5 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
}

}
**#Figures E2Ag-E2Ai: National government decides to close a department of a university (college).
{
*IMPORTANT NOTE: Set the base categories for tier and treatment_nr to get estimates for the county taking a decision.

xtreg second_answer ib(1).tier i.first_answer##ib(6).treatment_nr##c.sf1, fe cluster(id)

*Figure E2Ag: National government decides to close a department of a university (college) and municipality supports.
{
margins, at( tier=1 first_answer=1 treatment_nr==3 sf1=( 2.5 3.93 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==3 sf1=( 2.5 3.93 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==3 sf1=( 2.5 3.93 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==3 sf1=( 2.5 3.93 ) ) level(84) noestimcheck

margins, at( tier=1 first_answer=1 treatment_nr==3 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==3 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==3 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==3 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
}
*Figure E2Ah: National government decides to close a department of a university (college) and county supports.
{
margins, at( tier=1 first_answer=1 treatment_nr==1 sf1=( 2.5 3.93 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==1 sf1=( 2.5 3.93 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==1 sf1=( 2.5 3.93 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==1 sf1=( 2.5 3.93 ) ) level(84) noestimcheck

margins, at( tier=1 first_answer=1 treatment_nr==1 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==1 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==1 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==1 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
}
*Figure E2Ai: National government decides to close a department of a university (college) and municipality and county support.
{
margins, at( tier=1 first_answer=1 treatment_nr==4 sf1=( 2.5 3.93 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==4 sf1=( 2.5 3.93 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==4 sf1=( 2.5 3.93 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==4 sf1=( 2.5 3.93 ) ) level(84) noestimcheck

margins, at( tier=1 first_answer=1 treatment_nr==4 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==4 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==4 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==4 sf1=( 2.5 3.93 ) ) level(95) noestimcheck
}
}
}
**#Figure E2B. Model 2-Q2-Q4-Q6: Preference for self-rule item 2.
{
xtset id tier

*IMPORTANT NOTE: The point estimates are different from those produced by R and shown in the Supplementary Materials. This is because Stata and R take different (respondent) base categories. The differences between the point estimates are the same and lead to the same findings.

*The point estimates are statistically significantly different from each other at p<0.05 when their 84% CIs do not overlap and at p<0.01 when their 95% CIs do not overlap (Greenland et al. 2016; Julius 2004; Macgregor-Fors and Payton 2013). R and Stata produce slightly different CIs whereby estimates are different at the second (hundreths) or third (thousands) digit behind the decimal point. In some instances, this leads to some discrepancies between R and Stata in assigning statistical significance. In all cases of discrepancies, the R estimates are more conservative, i.e. R indicates non-significance and p<0.05 whereas Stata indicates respectively p<0.05 and p<0.01. The statistical significance levels shown in the Figures in the main text and in the Supplementary Materials are based on the more conservative R estimates. 

**#Figures E2Ba-E2Bc: Municipality decides to close kindergarten.
{
*IMPORTANT NOTE: Set the base categories for tier and treatment_nr to get estimates for the municipality taking a decision.

xtreg second_answer ib(3).tier i.first_answer##ib(3).treatment_nr##c.sf2, fe cluster(id)

*Figure E2Ba: Municipality decides to close kindergarten and county supports.
{
margins, at( tier=3 first_answer=1 treatment_nr==1 sf2=( 1.67 3.6 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==1 sf2=( 1.67 3.6 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==1 sf2=( 1.67 3.6 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==1 sf2=( 1.67 3.6 ) ) level(84) noestimcheck

margins, at( tier=3 first_answer=1 treatment_nr==1 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==1 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==1 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==1 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
}
*Figure E2Bb: Municipality decides to close kindergarten and national government supports.
{
margins, at( tier=3 first_answer=1 treatment_nr==6 sf2=( 1.67 3.6 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==6 sf2=( 1.67 3.6 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==6 sf2=( 1.67 3.6 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==6 sf2=( 1.67 3.6 ) ) level(84) noestimcheck

margins, at( tier=3 first_answer=1 treatment_nr==6 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==6 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==6 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==6 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
}
*Figure E2Bc: Municipality decides to close kindergarten and county and national government support.
{
margins, at( tier=3 first_answer=1 treatment_nr==2 sf2=( 1.67 3.6 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==2 sf2=( 1.67 3.6 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==2 sf2=( 1.67 3.6 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==2 sf2=( 1.67 3.6 ) ) level(84) noestimcheck

margins, at( tier=3 first_answer=1 treatment_nr==2 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==2 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==2 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==2 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
}
}
**#Figures E2Bd-E2Bf: County decides to close upper secondary school.
{
*IMPORTANT NOTE: Set the base categories for tier and treatment_nr to get estimates for the county taking a decision.

xtreg second_answer ib(2).tier i.first_answer##ib(1).treatment_nr##c.sf2, fe cluster(id)

*Figure E2Bd: County decides to close upper secondary school and municipality supports.
{
margins, at( tier=2 first_answer=1 treatment_nr==3 sf2=( 1.67 3.6 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==3 sf2=( 1.67 3.6 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==3 sf2=( 1.67 3.6 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==3 sf2=( 1.67 3.6 ) ) level(84) noestimcheck

margins, at( tier=2 first_answer=1 treatment_nr==3 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==3 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==3 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==3 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
}
*Figure E2Be: County decides to close upper secondary school and national government supports.
{
margins, at( tier=2 first_answer=1 treatment_nr==6 sf2=( 1.67 3.6 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==6 sf2=( 1.67 3.6 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==6 sf2=( 1.67 3.6 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==6 sf2=( 1.67 3.6 ) ) level(84) noestimcheck

margins, at( tier=2 first_answer=1 treatment_nr==6 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==6 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==6 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==6 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
}
*Figure E2Bf: County decides to close upper secondary school and municipality and national government support.
{
margins, at( tier=2 first_answer=1 treatment_nr==5 sf2=( 1.67 3.6 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==5 sf2=( 1.67 3.6 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==5 sf2=( 1.67 3.6 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==5 sf2=( 1.67 3.6 ) ) level(84) noestimcheck

margins, at( tier=2 first_answer=1 treatment_nr==5 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==5 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==5 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==5 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
}
}
**#Figures E2Bg-E2Bi: National government decides to close a department of a university (college).
{
*IMPORTANT NOTE: Set the base categories for tier and treatment_nr to get estimates for the county taking a decision.

xtreg second_answer ib(1).tier i.first_answer##ib(6).treatment_nr##c.sf2, fe cluster(id)

*Figure E2Bg: National government decides to close a department of a university (college) and municipality supports.
{
margins, at( tier=1 first_answer=1 treatment_nr==3 sf2=( 1.67 3.6 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==3 sf2=( 1.67 3.6 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==3 sf2=( 1.67 3.6 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==3 sf2=( 1.67 3.6 ) ) level(84) noestimcheck

margins, at( tier=1 first_answer=1 treatment_nr==3 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==3 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==3 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==3 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
}
*Figure E2Bh: National government decides to close a department of a university (college) and county supports.
{
margins, at( tier=1 first_answer=1 treatment_nr==1 sf2=( 1.67 3.6 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==1 sf2=( 1.67 3.6 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==1 sf2=( 1.67 3.6 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==1 sf2=( 1.67 3.6 ) ) level(84) noestimcheck

margins, at( tier=1 first_answer=1 treatment_nr==1 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==1 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==1 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==1 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
}
*Figure E2Bi: National government decides to close a department of a university (college) and municipality and county support.
{
margins, at( tier=1 first_answer=1 treatment_nr==4 sf2=( 1.67 3.6 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==4 sf2=( 1.67 3.6 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==4 sf2=( 1.67 3.6 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==4 sf2=( 1.67 3.6 ) ) level(84) noestimcheck

margins, at( tier=1 first_answer=1 treatment_nr==4 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==4 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==4 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==4 sf2=( 1.67 3.6 ) ) level(95) noestimcheck
}
}
}
**#Figure E2C. Model 2-Q2-Q4-Q6: Preference for self-rule item 3.
{

xtset id tier

*IMPORTANT NOTE: The point estimates are different from those produced by R and shown in the Supplementary Materials. This is because Stata and R take different (respondent) base categories. The differences between the point estimates are the same and lead to the same findings.

*The point estimates are statistically significantly different from each other at p<0.05 when their 84% CIs do not overlap and at p<0.01 when their 95% CIs do not overlap (Greenland et al. 2016; Julius 2004; Macgregor-Fors and Payton 2013). R and Stata produce slightly different CIs whereby estimates are different at the second (hundreths) or third (thousands) digit behind the decimal point. In some instances, this leads to some discrepancies between R and Stata in assigning statistical significance. In all cases of discrepancies, the R estimates are more conservative, i.e. R indicates non-significance and p<0.05 whereas Stata indicates respectively p<0.05 and p<0.01. The statistical significance levels shown in the Figures in the main text and in the Supplementary Materials are based on the more conservative R estimates. 

**#Figures E2Ca-E2Cc: Municipality decides to close kindergarten.
{
*IMPORTANT NOTE: Set the base categories for tier and treatment_nr to get estimates for the municipality taking a decision.

xtreg second_answer ib(3).tier i.first_answer##ib(3).treatment_nr##c.sf3, fe cluster(id)

*Figure E2Ca: Municipality decides to close kindergarten and county supports*.
{
margins, at( tier=3 first_answer=1 treatment_nr==1 sf3=( 2.07 3.75 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==1 sf3=( 2.07 3.75 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==1 sf3=( 2.07 3.75 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==1 sf3=( 2.07 3.75 ) ) level(84) noestimcheck

margins, at( tier=3 first_answer=1 treatment_nr==1 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==1 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==1 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==1 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
}
*Figure E2Cb: Municipality decides to close kindergarten and national government supports.
{
margins, at( tier=3 first_answer=1 treatment_nr==6 sf3=( 2.07 3.75 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==6 sf3=( 2.07 3.75 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==6 sf3=( 2.07 3.75 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==6 sf3=( 2.07 3.75 ) ) level(84) noestimcheck

margins, at( tier=3 first_answer=1 treatment_nr==6 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==6 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==6 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==6 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
}
*Figure E2Cc: Municipality decides to close kindergarten and county and national government support.
{
margins, at( tier=3 first_answer=1 treatment_nr==2 sf3=( 2.07 3.75 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==2 sf3=( 2.07 3.75 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==2 sf3=( 2.07 3.75 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==2 sf3=( 2.07 3.75 ) ) level(84) noestimcheck

margins, at( tier=3 first_answer=1 treatment_nr==2 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==2 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==2 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==2 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
}
}
**#Figures E2Cd-E2Cf: County decides to close upper secondary school.
{
*IMPORTANT NOTE: Set the base categories for tier and treatment_nr to get estimates for the county taking a decision.

xtreg second_answer ib(2).tier i.first_answer##ib(1).treatment_nr##c.sf3, fe cluster(id)

*Figure E2Cd: County decides to close upper secondary school and municipality supports.
{
margins, at( tier=2 first_answer=1 treatment_nr==3 sf3=( 2.07 3.75 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==3 sf3=( 2.07 3.75 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==3 sf3=( 2.07 3.75 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==3 sf3=( 2.07 3.75 ) ) level(84) noestimcheck

margins, at( tier=2 first_answer=1 treatment_nr==3 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==3 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==3 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==3 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
}
*Figure E2Ce: County decides to close upper secondary school and national government supports.
{
margins, at( tier=2 first_answer=1 treatment_nr==6 sf3=( 2.07 3.75 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==6 sf3=( 2.07 3.75 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==6 sf3=( 2.07 3.75 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==6 sf3=( 2.07 3.75 ) ) level(84) noestimcheck

margins, at( tier=2 first_answer=1 treatment_nr==6 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==6 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==6 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==6 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
}
*Figure E2Cf: County decides to close upper secondary school and municipality and national government support.
{
margins, at( tier=2 first_answer=1 treatment_nr==5 sf3=( 2.07 3.75 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==5 sf3=( 2.07 3.75 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==5 sf3=( 2.07 3.75 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==5 sf3=( 2.07 3.75 ) ) level(84) noestimcheck

margins, at( tier=2 first_answer=1 treatment_nr==5 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==5 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==5 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==5 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
}
}
**#Figures E2Cg-E2Ci: National government decides to close a department of a university (college).
{
*IMPORTANT NOTE: Set the base categories for tier and treatment_nr to get estimates for the county taking a decision.

xtreg second_answer ib(1).tier i.first_answer##ib(6).treatment_nr##c.sf3, fe cluster(id)

*Figure E2Cg: National government decides to close a department of a university (college) and municipality supports.
{
margins, at( tier=1 first_answer=1 treatment_nr==3 sf3=( 2.07 3.75 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==3 sf3=( 2.07 3.75 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==3 sf3=( 2.07 3.75 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==3 sf3=( 2.07 3.75 ) ) level(84) noestimcheck

margins, at( tier=1 first_answer=1 treatment_nr==3 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==3 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==3 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==3 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
}
*Figure E2Ch: National government decides to close a department of a university (college) and county supports.
{
margins, at( tier=1 first_answer=1 treatment_nr==1 sf3=( 2.07 3.75 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==1 sf3=( 2.07 3.75 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==1 sf3=( 2.07 3.75 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==1 sf3=( 2.07 3.75 ) ) level(84) noestimcheck

margins, at( tier=1 first_answer=1 treatment_nr==1 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==1 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==1 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==1 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
}
*Figure E2Ci: National government decides to close a department of a university (college) and municipality and county support.
{
margins, at( tier=1 first_answer=1 treatment_nr==4 sf3=( 2.07 3.75 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==4 sf3=( 2.07 3.75 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==4 sf3=( 2.07 3.75 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==4 sf3=( 2.07 3.75 ) ) level(84) noestimcheck

margins, at( tier=1 first_answer=1 treatment_nr==4 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==4 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==4 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==4 sf3=( 2.07 3.75 ) ) level(95) noestimcheck
}
}
}
**#Figure E2D. Model 2-Q2-Q4-Q6: Preference for shared rule item 4.
{
	
xtset id tier

*IMPORTANT NOTE: The point estimates are different from those produced by R and shown in the Supplementary Materials. This is because Stata and R take different (respondent) base categories. The differences between the point estimates are the same and lead to the same findings.

*The point estimates are statistically significantly different from each other at p<0.05 when their 84% CIs do not overlap and at p<0.01 when their 95% CIs do not overlap (Greenland et al. 2016; Julius 2004; Macgregor-Fors and Payton 2013). R and Stata produce slightly different CIs whereby estimates are different at the second (hundreths) or third (thousands) digit behind the decimal point. In some instances, this leads to some discrepancies between R and Stata in assigning statistical significance. In all cases of discrepancies, the R estimates are more conservative, i.e. R indicates non-significance and p<0.05 whereas Stata indicates respectively p<0.05 and p<0.01. The statistical significance levels shown in the Figures in the main text and in the Supplementary Materials are based on the more conservative R estimates. 

**#Figures E2Da-E2Dc: Municipality decides to close kindergarten.
{
*IMPORTANT NOTE: Set the base categories for tier and treatment_nr to get estimates for the municipality taking a decision.

xtreg second_answer ib(3).tier i.first_answer##ib(3).treatment_nr##c.sh1, fe cluster(id)

*Figure E2Da: Municipality decides to close kindergarten and county supports.
{
margins, at( tier=3 first_answer=1 treatment_nr==1 sh1=( 2.28 3.85 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==1 sh1=( 2.28 3.85 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==1 sh1=( 2.28 3.85 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==1 sh1=( 2.28 3.85 ) ) level(84) noestimcheck

margins, at( tier=3 first_answer=1 treatment_nr==1 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==1 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==1 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==1 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
}
*Figure E2Db: Municipality decides to close kindergarten and national government supports.
{
margins, at( tier=3 first_answer=1 treatment_nr==6 sh1=( 2.28 3.85 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==6 sh1=( 2.28 3.85 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==6 sh1=( 2.28 3.85 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==6 sh1=( 2.28 3.85 ) ) level(84) noestimcheck

margins, at( tier=3 first_answer=1 treatment_nr==6 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==6 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==6 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==6 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
}
*Figure E2Dc: Municipality decides to close kindergarten and county and national government support.
{
margins, at( tier=3 first_answer=1 treatment_nr==2 sh1=( 2.28 3.85 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==2 sh1=( 2.28 3.85 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==2 sh1=( 2.28 3.85 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==2 sh1=( 2.28 3.85 ) ) level(84) noestimcheck

margins, at( tier=3 first_answer=1 treatment_nr==2 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==2 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==2 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==2 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
}
}
**#Figures E2Dd-E2Df: County decides to close upper secondary school.
{
*IMPORTANT NOTE: Set the base categories for tier and treatment_nr to get estimates for the county taking a decision.

xtreg second_answer ib(2).tier i.first_answer##ib(1).treatment_nr##c.sh1, fe cluster(id)

*Figure E2Dd: County decides to close upper secondary school and municipality supports.
{
margins, at( tier=2 first_answer=1 treatment_nr==3 sh1=( 2.28 3.85 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==3 sh1=( 2.28 3.85 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==3 sh1=( 2.28 3.85 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==3 sh1=( 2.28 3.85 ) ) level(84) noestimcheck

margins, at( tier=2 first_answer=1 treatment_nr==3 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==3 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==3 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==3 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
}
*Figure E2De: County decides to close upper secondary school and national government supports.
{
margins, at( tier=2 first_answer=1 treatment_nr==6 sh1=( 2.28 3.85 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==6 sh1=( 2.28 3.85 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==6 sh1=( 2.28 3.85 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==6 sh1=( 2.28 3.85 ) ) level(84) noestimcheck

margins, at( tier=2 first_answer=1 treatment_nr==6 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==6 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==6 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==6 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
}
*Figure E2Df: County decides to close upper secondary school and municipality and national government support.
{
margins, at( tier=2 first_answer=1 treatment_nr==5 sh1=( 2.28 3.85 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==5 sh1=( 2.28 3.85 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==5 sh1=( 2.28 3.85 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==5 sh1=( 2.28 3.85 ) ) level(84) noestimcheck

margins, at( tier=2 first_answer=1 treatment_nr==5 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==5 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==5 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==5 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
}
}
**#Figures E2Dg-E2Di: National government decides to close a department of a university (college).
{
*IMPORTANT NOTE: Set the base categories for tier and treatment_nr to get estimates for the county taking a decision.

xtreg second_answer ib(1).tier i.first_answer##ib(6).treatment_nr##c.sh1, fe cluster(id)

*Figure E2Dg: National government decides to close a department of a university (college) and municipality supports.
{
margins, at( tier=1 first_answer=1 treatment_nr==3 sh1=( 2.28 3.85 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==3 sh1=( 2.28 3.85 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==3 sh1=( 2.28 3.85 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==3 sh1=( 2.28 3.85 ) ) level(84) noestimcheck

margins, at( tier=1 first_answer=1 treatment_nr==3 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==3 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==3 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==3 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
}
*Figure E2Dh: National government decides to close a department of a university (college) and county supports.
{
margins, at( tier=1 first_answer=1 treatment_nr==1 sh1=( 2.28 3.85 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==1 sh1=( 2.28 3.85 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==1 sh1=( 2.28 3.85 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==1 sh1=( 2.28 3.85 ) ) level(84) noestimcheck

margins, at( tier=1 first_answer=1 treatment_nr==1 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==1 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==1 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==1 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
}
*Figure E2Di: National government decides to close a department of a university (college) and municipality and county support.
{
margins, at( tier=1 first_answer=1 treatment_nr==4 sh1=( 2.28 3.85 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==4 sh1=( 2.28 3.85 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==4 sh1=( 2.28 3.85 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==4 sh1=( 2.28 3.85 ) ) level(84) noestimcheck

margins, at( tier=1 first_answer=1 treatment_nr==4 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==4 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==4 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==4 sh1=( 2.28 3.85 ) ) level(95) noestimcheck
}
}
}
**#Figure E2E. Model 2-Q2-Q4-Q6: Preference for shared rule item 5.
{

xtset id tier

*IMPORTANT NOTE: The point estimates are different from those produced by R and shown in the Supplementary Materials. This is because Stata and R take different (respondent) base categories. The differences between the point estimates are the same and lead to the same findings.

*The point estimates are statistically significantly different from each other at p<0.05 when their 84% CIs do not overlap and at p<0.01 when their 95% CIs do not overlap (Greenland et al. 2016; Julius 2004; Macgregor-Fors and Payton 2013). R and Stata produce slightly different CIs whereby estimates are different at the second (hundreths) or third (thousands) digit behind the decimal point. In some instances, this leads to some discrepancies between R and Stata in assigning statistical significance. In all cases of discrepancies, the R estimates are more conservative, i.e. R indicates non-significance and p<0.05 whereas Stata indicates respectively p<0.05 and p<0.01. The statistical significance levels shown in the Figures in the main text and in the Supplementary Materials are based on the more conservative R estimates. 

**#Figures E2Ea-E2Ec: Municipality decides to close kindergarten.
{
*IMPORTANT NOTE: Set the base categories for tier and treatment_nr to get estimates for the municipality taking a decision.

xtreg second_answer ib(3).tier i.first_answer##ib(3).treatment_nr##c.sh2, fe cluster(id)

*Figure E2Ea: Municipality decides to close kindergarten and county supports.
{
margins, at( tier=3 first_answer=1 treatment_nr==1 sh2=( 2.72 4 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==1 sh2=( 2.72 4 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==1 sh2=( 2.72 4 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==1 sh2=( 2.72 4 ) ) level(84) noestimcheck

margins, at( tier=3 first_answer=1 treatment_nr==1 sh2=( 2.72 4 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==1 sh2=( 2.72 4 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==1 sh2=( 2.72 4 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==1 sh2=( 2.72 4 ) ) level(95) noestimcheck
}
*Figure E2Eb: Municipality decides to close kindergarten and national government supports.
{
margins, at( tier=3 first_answer=1 treatment_nr==6 sh2=( 2.72 4 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==6 sh2=( 2.72 4 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==6 sh2=( 2.72 4 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==6 sh2=( 2.72 4 ) ) level(84) noestimcheck

margins, at( tier=3 first_answer=1 treatment_nr==6 sh2=( 2.72 4 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==6 sh2=( 2.72 4 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==6 sh2=( 2.72 4 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==6 sh2=( 2.72 4 ) ) level(95) noestimcheck
}
*Figure E2Ec: Municipality decides to close kindergarten and county and national government support.
{
margins, at( tier=3 first_answer=1 treatment_nr==2 sh2=( 2.72 4 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==2 sh2=( 2.72 4 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==2 sh2=( 2.72 4 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==2 sh2=( 2.72 4 ) ) level(84) noestimcheck

margins, at( tier=3 first_answer=1 treatment_nr==2 sh2=( 2.72 4 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==2 sh2=( 2.72 4 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==2 sh2=( 2.72 4 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==2 sh2=( 2.72 4 ) ) level(95) noestimcheck
}
}
**#Figures E2Ed-E2Ef: County decides to close upper secondary school.
{
*IMPORTANT NOTE: Set the base categories for tier and treatment_nr to get estimates for the county taking a decision.

xtreg second_answer ib(2).tier i.first_answer##ib(1).treatment_nr##c.sh2, fe cluster(id)

*Figure E2Ed: County decides to close upper secondary school and municipality supports.
{
margins, at( tier=2 first_answer=1 treatment_nr==3 sh2=( 2.72 4 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==3 sh2=( 2.72 4 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==3 sh2=( 2.72 4 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==3 sh2=( 2.72 4 ) ) level(84) noestimcheck

margins, at( tier=2 first_answer=1 treatment_nr==3 sh2=( 2.72 4 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==3 sh2=( 2.72 4 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==3 sh2=( 2.72 4 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==3 sh2=( 2.72 4 ) ) level(95) noestimcheck
}
*Figure E2Ee: County decides to close upper secondary school and national government supports.
{
margins, at( tier=2 first_answer=1 treatment_nr==6 sh2=( 2.72 4 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==6 sh2=( 2.72 4 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==6 sh2=( 2.72 4 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==6 sh2=( 2.72 4 ) ) level(84) noestimcheck

margins, at( tier=2 first_answer=1 treatment_nr==6 sh2=( 2.72 4 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==6 sh2=( 2.72 4 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==6 sh2=( 2.72 4 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==6 sh2=( 2.72 4 ) ) level(95) noestimcheck
}
*Figure E2Ef: County decides to close upper secondary school and municipality and national government support.
{
margins, at( tier=2 first_answer=1 treatment_nr==5 sh2=( 2.72 4 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==5 sh2=( 2.72 4 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==5 sh2=( 2.72 4 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==5 sh2=( 2.72 4 ) ) level(84) noestimcheck

margins, at( tier=2 first_answer=1 treatment_nr==5 sh2=( 2.72 4 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==5 sh2=( 2.72 4 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==5 sh2=( 2.72 4 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==5 sh2=( 2.72 4 ) ) level(95) noestimcheck
}
}
**#Figures E2Eg-E2Ei: National government decides to close a department of a university (college).
{
*IMPORTANT NOTE: Set the base categories for tier and treatment_nr to get estimates for the county taking a decision.

xtreg second_answer ib(1).tier i.first_answer##ib(6).treatment_nr##c.sh2, fe cluster(id)

*Figure E2Eg: National government decides to close a department of a university (college) and municipality supports.
{
margins, at( tier=1 first_answer=1 treatment_nr==3 sh2=( 2.72 4 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==3 sh2=( 2.72 4 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==3 sh2=( 2.72 4 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==3 sh2=( 2.72 4 ) ) level(84) noestimcheck

margins, at( tier=1 first_answer=1 treatment_nr==3 sh2=( 2.72 4 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==3 sh2=( 2.72 4 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==3 sh2=( 2.72 4 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==3 sh2=( 2.72 4 ) ) level(95) noestimcheck
}
*Figure E2Eh: National government decides to close a department of a university (college) and county supports.
{
margins, at( tier=1 first_answer=1 treatment_nr==1 sh2=( 2.72 4 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==1 sh2=( 2.72 4 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==1 sh2=( 2.72 4 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==1 sh2=( 2.72 4 ) ) level(84) noestimcheck

margins, at( tier=1 first_answer=1 treatment_nr==1 sh2=( 2.72 4 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==1 sh2=( 2.72 4 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==1 sh2=( 2.72 4 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==1 sh2=( 2.72 4 ) ) level(95) noestimcheck
}
*Figure E2Ei: National government decides to close a department of a university (college) and municipality and county support.
{
margins, at( tier=1 first_answer=1 treatment_nr==4 sh2=( 2.72 4 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==4 sh2=( 2.72 4 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==4 sh2=( 2.72 4 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==4 sh2=( 2.72 4 ) ) level(84) noestimcheck

margins, at( tier=1 first_answer=1 treatment_nr==4 sh2=( 2.72 4 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==4 sh2=( 2.72 4 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==4 sh2=( 2.72 4 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==4 sh2=( 2.72 4 ) ) level(95) noestimcheck
}
}
}
**#Figure E2F. Model 2-Q2-Q4-Q6: Preference for shared rule item 6.
{
xtset id tier

*IMPORTANT NOTE: The point estimates are different from those produced by R and shown in the Supplementary Materials. This is because Stata and R take different (respondent) base categories. The differences between the point estimates are the same and lead to the same findings.

*The point estimates are statistically significantly different from each other at p<0.05 when their 84% CIs do not overlap and at p<0.01 when their 95% CIs do not overlap (Greenland et al. 2016; Julius 2004; Macgregor-Fors and Payton 2013). R and Stata produce slightly different CIs whereby estimates are different at the second (hundreths) or third (thousands) digit behind the decimal point. In some instances, this leads to some discrepancies between R and Stata in assigning statistical significance. In all cases of discrepancies, the R estimates are more conservative, i.e. R indicates non-significance and p<0.05 whereas Stata indicates respectively p<0.05 and p<0.01. The statistical significance levels shown in the Figures in the main text and in the Supplementary Materials are based on the more conservative R estimates. 

**#Figures E2Fa-E2Fc: Municipality decides to close kindergarten.
{
*IMPORTANT NOTE: Set the base categories for tier and treatment_nr to get estimates for the municipality taking a decision.

xtreg second_answer ib(3).tier i.first_answer##ib(3).treatment_nr##c.sh3, fe cluster(id)

*Figure E2Fa: Municipality decides to close kindergarten and county supports.
{
margins, at( tier=3 first_answer=1 treatment_nr==1 sh3=( 2.57 3.92 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==1 sh3=( 2.57 3.92 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==1 sh3=( 2.57 3.92 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==1 sh3=( 2.57 3.92 ) ) level(84) noestimcheck

margins, at( tier=3 first_answer=1 treatment_nr==1 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==1 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==1 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==1 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
}
*Figure E2Fb: Municipality decides to close kindergarten and national government supports.
{
margins, at( tier=3 first_answer=1 treatment_nr==6 sh3=( 2.57 3.92 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==6 sh3=( 2.57 3.92 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==6 sh3=( 2.57 3.92 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==6 sh3=( 2.57 3.92 ) ) level(84) noestimcheck

margins, at( tier=3 first_answer=1 treatment_nr==6 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==6 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==6 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==6 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
}
*Figure E2Fc: Municipality decides to close kindergarten and county and national government support.
{
margins, at( tier=3 first_answer=1 treatment_nr==2 sh3=( 2.57 3.92 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==2 sh3=( 2.57 3.92 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==2 sh3=( 2.57 3.92 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==2 sh3=( 2.57 3.92 ) ) level(84) noestimcheck

margins, at( tier=3 first_answer=1 treatment_nr==2 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==2 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==2 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==2 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
}
}
**#Figures E2Fd-E2Ff: County decides to close upper secondary school.
{
*IMPORTANT NOTE: Set the base categories for tier and treatment_nr to get estimates for the county taking a decision.

xtreg second_answer ib(2).tier i.first_answer##ib(1).treatment_nr##c.sh3, fe cluster(id)

*Figure E2Fd: County decides to close upper secondary school and municipality supports.
{
margins, at( tier=2 first_answer=1 treatment_nr==3 sh3=( 2.57 3.92 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==3 sh3=( 2.57 3.92 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==3 sh3=( 2.57 3.92 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==3 sh3=( 2.57 3.92 ) ) level(84) noestimcheck

margins, at( tier=2 first_answer=1 treatment_nr==3 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==3 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==3 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==3 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
}
*Figure E2Fe: County decides to close upper secondary school and national government supports.
{
margins, at( tier=2 first_answer=1 treatment_nr==6 sh3=( 2.57 3.92 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==6 sh3=( 2.57 3.92 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==6 sh3=( 2.57 3.92 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==6 sh3=( 2.57 3.92 ) ) level(84) noestimcheck

margins, at( tier=2 first_answer=1 treatment_nr==6 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==6 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==6 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==6 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
}
*Figure E2Ff: County decides to close upper secondary school and municipality and national government support.
{
margins, at( tier=2 first_answer=1 treatment_nr==5 sh3=( 2.57 3.92 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==5 sh3=( 2.57 3.92 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==5 sh3=( 2.57 3.92 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==5 sh3=( 2.57 3.92 ) ) level(84) noestimcheck

margins, at( tier=2 first_answer=1 treatment_nr==5 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==5 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==5 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==5 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
}
}
**#Figures E2Fg-E2Fi: National government decides to close a department of a university (college).
{
*IMPORTANT NOTE: Set the base categories for tier and treatment_nr to get estimates for the county taking a decision.

xtreg second_answer ib(1).tier i.first_answer##ib(6).treatment_nr##c.sh3, fe cluster(id)

*Figure E2Fg: National government decides to close a department of a university (college) and municipality supports.
{
margins, at( tier=1 first_answer=1 treatment_nr==3 sh3=( 2.57 3.92 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==3 sh3=( 2.57 3.92 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==3 sh3=( 2.57 3.92 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==3 sh3=( 2.57 3.92 ) ) level(84) noestimcheck

margins, at( tier=1 first_answer=1 treatment_nr==3 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==3 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==3 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==3 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
}
*Figure E2Fh: National government decides to close a department of a university (college) and county supports.
{
margins, at( tier=1 first_answer=1 treatment_nr==1 sh3=( 2.57 3.92 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==1 sh3=( 2.57 3.92 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==1 sh3=( 2.57 3.92 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==1 sh3=( 2.57 3.92 ) ) level(84) noestimcheck

margins, at( tier=1 first_answer=1 treatment_nr==1 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==1 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==1 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==1 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
}
*Figure E2Fi: National government decides to close a department of a university (college) and municipality and county support.
{
margins, at( tier=1 first_answer=1 treatment_nr==4 sh3=( 2.57 3.92 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==4 sh3=( 2.57 3.92 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==4 sh3=( 2.57 3.92 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==4 sh3=( 2.57 3.92 ) ) level(84) noestimcheck

margins, at( tier=1 first_answer=1 treatment_nr==4 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==4 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==4 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==4 sh3=( 2.57 3.92 ) ) level(95) noestimcheck
}
}
}
}

{APPENDIX F. Appendix F. Robustness test III: Random-effects model.

*IMPORTANT NOTE: Appendix F has its own replication dataset; OPEN Berkay_Schakel_2025_JPP_replication_RE_dataset.dta

{The following changes have been implemented to prepare the dataset produced by R for use in Stata

encode(responseid_hash), gen(id)
encode(municipality_hash), gen(id_mun)

*tier is a recoded variable that traces the decision_maker: 1=N; 2=C; 3=M.
*treatment_nr is a recoded variable that traces the 'support treatment': 1=C; 2=C+N; 3=M; 4=M+C; 5=M+N; 6=N.
}

{Table F1. Model 1-Q1-Q3-Q5: Random effects model specification.


**IMPORTANT NOTE: Set the base categories for tier and treatment_nr to get estimates for the national government taking a decision.

mixed first_answer i.tier##c.psa gender i.age edu i.incm i.urb_rur i.econ_reg i.left_right have_chldrn_dum || id_mun: || id:

}

{Figure F1. Model 1-Q1-Q3-Q5: Random effects model specification.

*IMPORTANT NOTE: The point estimates are different from those produced by R and shown in the Supplementary Materials. This is because Stata and R take different (respondent) base categories. The differences between the point estimates are the same and lead to the same findings.

*The point estimates are statistically significantly different from each other at p<0.05 when their 84% CIs do not overlap and at p<0.01 when their 95% CIs do not overlap (Greenland et al. 2016; Julius 2004; Macgregor-Fors and Payton 2013). R and Stata produce slightly different CIs whereby estimates are different at the second (hundreths) or third (thousands) digit behind the decimal point. In some instances, this leads to some discrepancies between R and Stata in assigning statistical significance. In all cases of discrepancies, the R estimates are more conservative, i.e. R indicates non-significance and p<0.05 whereas Stata indicates respectively p<0.05 and p<0.01. The statistical significance levels shown in the Figures in the main text and in the Supplementary Materials are based on the more conservative R estimates. 

**IMPORTANT NOTE: Set the base categories for tier and treatment_nr to get estimates for the national government taking a decision.

mixed first_answer ib(1).tier##c.psa gender i.age edu i.incm i.urb_rur i.econ_reg i.left_right have_chldrn_dum || id_mun: || id:

margins, at( tier=( 1 2 3 ) psa=( 0 1 ) ) level(84) noestimcheck
margins, at( tier=( 1 2 3 ) psa=( 0 1 ) ) level(95) noestimcheck

margins, at( tier=( 1 2 3 ) psa=( 0 (0.1) 1 ) ) level(84) noestimcheck
}

{Table F2. Model 2-Q2-Q4-Q6: Random effects model specification.

**IMPORTANT NOTE: Set the base categories for tier and treatment_nr to get estimates for the national government taking a decision.

mixed second_answer ib(1).tier i.first_answer##ib(6).treatment_nr##c.psa gender i.age edu i.incm i.urb_rur i.econ_reg i.left_right have_chldrn_dum || id_mun: || id:
}

{Figure F2. Model 2-Q2-Q4-Q6: Random effects model specification.

**#Figures F2A-C: Municipality decides to close kindergarten.

*IMPORTANT NOTE: The point estimates are different from those produced by R and shown in the Supplementary Materials. This is because Stata and R take different (respondent) base categories. The differences between the point estimates are the same and lead to the same findings.

*The point estimates are statistically significantly different from each other at p<0.05 when their 84% CIs do not overlap and at p<0.01 when their 95% CIs do not overlap (Greenland et al. 2016; Julius 2004; Macgregor-Fors and Payton 2013). R and Stata produce slightly different CIs whereby estimates are different at the second (hundreths) or third (thousands) digit behind the decimal point. In some instances, this leads to some discrepancies between R and Stata in assigning statistical significance. In all cases of discrepancies, the R estimates are more conservative, i.e. R indicates non-significance and p<0.05 whereas Stata indicates respectively p<0.05 and p<0.01. The statistical significance levels shown in the Figures in the main text and in the Supplementary Materials are based on the more conservative R estimates. 

**IMPORTANT NOTE: Set the base categories for tier and treatment_nr to get estimates for the municipality taking a decision.

mixed second_answer ib(3).tier i.first_answer##ib(3).treatment_nr##c.psa gender i.age edu i.incm i.urb_rur i.econ_reg i.left_right have_chldrn_dum || id_mun: || id:

{
*Figure F2A: Municipality decides to close kindergarten and county supports.
{
margins, at( tier=3 first_answer=1 treatment_nr==1 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==1 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==1 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==1 psa=( 0.44 0.88 ) ) level(84) noestimcheck

margins, at( tier=3 first_answer=1 treatment_nr==1 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==1 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==1 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==1 psa=( 0.44 0.88 ) ) level(95) noestimcheck
}
*Figure F2B: Municipality decides to close kindergarten and national government supports.
{
margins, at( tier=3 first_answer=1 treatment_nr==6 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==6 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==6 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==6 psa=( 0.44 0.88 ) ) level(84) noestimcheck

margins, at( tier=3 first_answer=1 treatment_nr==6 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==6 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==6 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==6 psa=( 0.44 0.88 ) ) level(95) noestimcheck
}
*Figure F2C: Municipality decides to close kindergarten and county and national government support.
{
margins, at( tier=3 first_answer=1 treatment_nr==2 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==2 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==2 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==2 psa=( 0.44 0.88 ) ) level(84) noestimcheck

margins, at( tier=3 first_answer=1 treatment_nr==2 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=2 treatment_nr==2 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=3 treatment_nr==2 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=3 first_answer=4 treatment_nr==2 psa=( 0.44 0.88 ) ) level(95) noestimcheck
}
}
**#Figures F2D-F: County decides to close upper secondary school.
{
*IMPORTANT NOTE: The point estimates are different from those produced by R and shown in the Supplementary Materials. This is because Stata and R take different (respondent) base categories. The differences between the point estimates are the same and lead to the same findings.

*The point estimates are statistically significantly different from each other at p<0.05 when their 84% CIs do not overlap and at p<0.01 when their 95% CIs do not overlap (Greenland et al. 2016; Julius 2004; Macgregor-Fors and Payton 2013). R and Stata produce slightly different CIs whereby estimates are different at the second (hundreths) or third (thousands) digit behind the decimal point. In some instances, this leads to some discrepancies between R and Stata in assigning statistical significance. In all cases of discrepancies, the R estimates are more conservative, i.e. R indicates non-significance and p<0.05 whereas Stata indicates respectively p<0.05 and p<0.01. The statistical significance levels shown in the Figures in the main text and in the Supplementary Materials are based on the more conservative R estimates. 

**IMPORTANT NOTE: Set the base categories for tier and treatment_nr to get estimates for the county taking a decision.

mixed second_answer ib(2).tier i.first_answer##ib(1).treatment_nr##c.psa gender i.age edu i.incm i.urb_rur i.econ_reg i.left_right have_chldrn_dum || id_mun: || id:

*Figure F2D: County decides to close upper secondary school and municipality supports.
{
margins, at( tier=2 first_answer=1 treatment_nr==3 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==3 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==3 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==3 psa=( 0.44 0.88 ) ) level(84) noestimcheck

margins, at( tier=2 first_answer=1 treatment_nr==3 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==3 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==3 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==3 psa=( 0.44 0.88 ) ) level(95) noestimcheck
}
*Figure F2E: County decides to close upper secondary school and national government supports.
{
margins, at( tier=2 first_answer=1 treatment_nr==6 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==6 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==6 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==6 psa=( 0.44 0.88 ) ) level(84) noestimcheck

margins, at( tier=2 first_answer=1 treatment_nr==6 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==6 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==6 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==6 psa=( 0.44 0.88 ) ) level(95) noestimcheck
}
*Figure F2F: County decides to close upper secondary school and municipality and national government support.
{
margins, at( tier=2 first_answer=1 treatment_nr==5 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==5 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==5 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==5 psa=( 0.44 0.88 ) ) level(84) noestimcheck

margins, at( tier=2 first_answer=1 treatment_nr==5 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=2 treatment_nr==5 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=3 treatment_nr==5 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=2 first_answer=4 treatment_nr==5 psa=( 0.44 0.88 ) ) level(95) noestimcheck
}
}
**#Figures F2G-I: National government decides to close a department of a university (college).
{
*IMPORTANT NOTE: The point estimates are different from those produced by R and shown in the Supplementary Materials. This is because Stata and R take different (respondent) base categories. The differences between the point estimates are the same and lead to the same findings.

*The point estimates are statistically significantly different from each other at p<0.05 when their 84% CIs do not overlap and at p<0.01 when their 95% CIs do not overlap (Greenland et al. 2016; Julius 2004; Macgregor-Fors and Payton 2013). R and Stata produce slightly different CIs whereby estimates are different at the second (hundreths) or third (thousands) digit behind the decimal point. In some instances, this leads to some discrepancies between R and Stata in assigning statistical significance. In all cases of discrepancies, the R estimates are more conservative, i.e. R indicates non-significance and p<0.05 whereas Stata indicates respectively p<0.05 and p<0.01. The statistical significance levels shown in the Figures in the main text and in the Supplementary Materials are based on the more conservative R estimates. 

**IMPORTANT NOTE: Set the base categories for tier and treatment_nr to get estimates for the national government taking a decision.

mixed second_answer ib(1).tier i.first_answer##ib(6).treatment_nr##c.psa gender i.age edu i.incm i.urb_rur i.econ_reg i.left_right have_chldrn_dum || id_mun: || id:

*Figure F2G: National government decides to close a department of a university (college) and municipality supports.
{
margins, at( tier=1 first_answer=1 treatment_nr==3 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==3 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==3 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==3 psa=( 0.44 0.88 ) ) level(84) noestimcheck

margins, at( tier=1 first_answer=1 treatment_nr==3 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==3 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==3 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==3 psa=( 0.44 0.88 ) ) level(95) noestimcheck
}
*Figure F2H: National govenrment decides to close a department of a university (college) and county supports.
{
margins, at( tier=1 first_answer=1 treatment_nr==1 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==1 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==1 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==1 psa=( 0.44 0.88 ) ) level(84) noestimcheck

margins, at( tier=1 first_answer=1 treatment_nr==1 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==1 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==1 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==1 psa=( 0.44 0.88 ) ) level(95) noestimcheck
}
*Figure F2I: National govenrment decides to close a department of a university (college) and municipality and county supports.
{
margins, at( tier=1 first_answer=1 treatment_nr==4 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==4 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==4 psa=( 0.44 0.88 ) ) level(84) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==4 psa=( 0.44 0.88 ) ) level(84) noestimcheck

margins, at( tier=1 first_answer=1 treatment_nr==4 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=2 treatment_nr==4 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=3 treatment_nr==4 psa=( 0.44 0.88 ) ) level(95) noestimcheck
margins, at( tier=1 first_answer=4 treatment_nr==4 psa=( 0.44 0.88 ) ) level(95) noestimcheck
}
}
} 
}

